Intern developing machine learning tools within ADEO Services' AI Factory team. Collaborating on software quality and automation alongside a senior ML engineer.
Responsibilities
As part of the AI Factory squad, your role is to turn Data Scientists' proof-of-concepts into robust production services.
Actively participate in code reviews to adopt and promote team standards (clean code, unit and integration tests).
Help optimize our inference services using FastAPI and our batch serving processes.
Contribute to the development of our internal toolbox based on ZenML.
Maintain and improve our internal Python library dedicated to automated deployments (online serving).
Provide Data Scientists with validation and testing tools to secure every production release.
Automate the full pipeline, from data fetching to the Model Registry.
Containerize services and gain experience managing deployments on our Kubernetes clusters using our orchestration tools.
Build skills in configuring trusted environments (Service Accounts, IAM roles on GCP) and automating infrastructure with Terraform.
Requirements
Student in an engineering program or equivalent (minimum Bachelor's level) specializing in Computer Science / AI.
You consider yourself more of a developer building tools for ML than an algorithms researcher.
Proficient in Python and experienced with Docker.
Some experience with CI/CD (GitHub Actions) and a strong commitment to clean code.
Understand how an orchestrator works (ZenML, Airflow, or Kubeflow) and know how to query BigQuery.
Interested in Kubernetes and Terraform; eager to move from theory to hands-on work on production clusters.
Critical thinker: comfortable challenging code in reviews while remaining constructive and solution-oriented.
Service-oriented: you enjoy creating tools and libraries that make life easier for others (Data Scientists).
Benefits
Meaningful assignments and mentoring throughout your internship/apprenticeship by a subject-matter expert in your learning area — in short, join a learning organization!
A Stud'ADEO community run by students themselves and the Campus team.
Dedicated support to help your employability after the apprenticeship/internship.
13th month salary, profit-sharing bonus and benefits, works council (CSE) perks, meal vouchers, etc.
An innovative workplace that inspires creativity and promotes collaboration: Terradeo!
Machine Learning Engineer designing and optimizing deep learning models for safety - critical environments at Destinus. Shaping the future of high - speed, autonomous flight technologies.
Machine Learning Engineer optimizing personalization systems for Spotify's audio streaming service. Collaborating with cross - functional teams to enhance user experience and deliver recommendations.
Principal Machine Learning Engineer developing ML and GenAI solutions in a cloud - native environment at Flexera. Leading a high - impact team and driving operational excellence for ML infrastructure.
Senior ML Platform/Ops Engineer building ML systems for AI - powered learning at Preply. Productionizing machine learning with high reliability, performance, and observability in a hybrid environment.
Senior ML Platform/Ops Engineer building AI - powered ML pipelines for a dynamic Ed - Tech company. Collaborating with ML scientists and engineers to ensure reliable deployment and observability.
Machine Learning Engineer developing advanced Deep Learning models for autonomous driving technology at Mobileye. Collaborating in a high - end algorithmic engineering team on critical computer vision challenges.
Machine Learning Engineer focusing on vulnerabilities and security of AI systems at Carnegie Mellon University. Collaborating with a team to build robust prototypes and provide solutions for government sponsors.
Lead machine learning engineer developing solutions for Army enterprise AI and ML team. Collaborating with experts to deliver cutting - edge analytics and models for real - world challenges.
Machine Learning & Signal Processing Scientist at BlueGreen Water Technologies analyzing multi - source environmental data. Focused on developing algorithms and models for signal processing and machine learning techniques.